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Assessing Tomato Flavors Chemically: Identification of Aroma Volatiles from Heirloom and Commercial Tomatoes using Solid-Phase Microextraction and GC-MS

芳香 固相微萃取 风味 色谱法 气相色谱法 气相色谱-质谱法 化学 食品科学 质谱法
作者
Ryanne Ballard,Arielle Benyo,Rachel Ren,Jasmine Nguyen,Joline Nguyen,Erica Zieber,Glen C. Gullickson,Hyung J. Kim
出处
期刊:Journal of Chemical Education [American Chemical Society]
卷期号:100 (3): 1263-1269 被引量:5
标识
DOI:10.1021/acs.jchemed.2c00815
摘要

The aroma volatile compounds in tomatoes are strongly linked to taste and flavor. Commercial tomato varieties, however, generally lack flavor. Tomato breeding programs have therefore started to focus on increasing the flavor-enhancing volatiles, which can be more easily altered than the sugar content. Analytical efforts that identify key flavor volatiles are therefore an important part of tomato improvement programs. Based on this real-world goal, an upper-level undergraduate analytical laboratory exercise has been developed that utilizes solid-phase microextraction (SPME) and gas chromatography–mass spectrometry to characterize tomato volatiles. Students adsorb volatile organic compounds (VOCs) and a spiked internal standard onto SPME fibers, and these volatiles are thermally desorbed in a gas chromatograph–mass spectrometer (GC-MS) for identification and quantification. VOCs from hybrid heirloom tomatoes (open-pollinated, older cultivars not bred for industrial agriculture) and store-bought tomatoes are compared and normalized to the internal standard, and students determine which volatiles contribute to positive (and negative) taste perceptions. The laboratory exercise has been employed successfully in our Instrumental Analysis course over three years. Using previously obtained data, the exercise was also modified for remote (Zoom) laboratory learning. The experimental protocol is robust with few technical issues, and students produce replicable data. Because students can relate easily to the subject matter of the research, engagement is high. A large, complex data set is produced in this exercise, and students learn to draw conclusions from this data set that mimic real-world, authentic research.
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